Seth D. Hannah asked
> > Can someone help me with creating a composite measure of
> prejudice from
> > four individual variables in my data set which measure prejudice.
> > the variables are:
> >
> > deasyblk: perception of blacks as easy to get along with
> > dwelfblk: perception of blacks as likely to be on welfare
> > dintlblk: perception of blacks as intelligent
> > drichblk: perception of blacks as rich or poor
> >
> > the variables are distributed as follows:
> >
> > . tab deasyblk
> >
> > easy to get along |
> > w/blacks | Freq. Percent
> Cum.
> > ---------------------+-----------------------------------
> > easy to get along w/ | 915 10.26 10.26
> > 2 | 1052
> 11.80 22.06
> > 3 | 1379
> 15.47 37.53
> > neither | 2722 30.53
> 68.06
> > 5 | 1143
> 12.82 80.88
> > 6 | 638
> 7.16 88.03
> > hard to get along w/ | 547 6.14 94.17
> > don't know... | 418 4.69 98.86
> > missing | 102 1.14 100.00
> > ---------------------+-----------------------------------
> > Total | 8916 100.00
> >
> > . tab dwelfblk
> >
> > self-supporting: |
> > blacks | Freq. Percent Cum.
> > --------------------+-----------------------------------
> > prefer self-support | 754 8.46 8.46
> > 2 | 521
> 5.84 14.30
> > 3 | 879
> 9.86 24.16
> > neither | 2132 23.91 48.07
> > 5 | 1723
> 19.32 67.40
> > 6 | 1332 14.94
> 82.34
> > prefer welfare | 1046 11.73 94.07
> > don't know... | 425 4.77 98.83
> > missing | 104 1.17 100.00
> > --------------------+-----------------------------------
> > Total | 8916 100.00
> >
> > . tab dintlblk
> >
> > intelligence: |
> > blacks | Freq. Percent Cum.
> > --------------+-----------------------------------
> > intelligent | 723 8.11 8.11
> > 2 | 807 9.05 17.16
> > 3 | 1597 17.91 35.07
> > neither | 3259 36.55 71.62
> > 5 | 1255 14.08 85.70
> > 6 | 479 5.37 91.0
> > unintelligent | 207 2.32 93.39
> > don't know... | 481 5.39 98.79
> > missing | 108 1.21 100.00
> > --------------+-----------------------------------
> > Total | 8916 100.00
> >
> > . tab drichblk
> >
> > rich-poor: |
> > blacks | Freq. Percent Cum.
> > --------------+-----------------------------------
> > rich | 59 0.66 0.66
> > 2 | 193 2.16 2.83
> > 3 | 499 5.60 8.42
> > neither | 2101 23.56 31.99
> > 5 | 2506 28.11 60.09
> > 6 | 2137 23.97 84.06
> > poor | 970 10.88 94.9
> > don't know... | 371 4.16 99.10
> > missing | 80 0.90 100.00
> > --------------+-----------------------------------
> > Total | 8916 100.00
> >
> > What I want to do is combine these four variables into
> one measure of
> > prejudice, which will become a dependent variable in some
> of my models.
> >
> > The only way I could think to do it was to create a new
> variable prejblk
> > with numerical values 1 through 7 that equal the sums of
> the respective
> > 1 through 7's
> > from my four variables...
> >
> > gen prejblk=.
> > replace prejblk=1 if
> drichblk==1|dwelfblk==1|deasyblk==1|dintlblk==1
> > replace prejblk=2 if
> drichblk==2|dwelfblk==2|deasyblk==2|dintlblk==2
> > etc.
> >
> > somehow this doesn't seem right, please help!
Bo Cutter
> As a first step you may want to look at a factor analysis (Principal
> components). This analysis will look at how and whether
> you can reduce your
> 5 variables into one or more variables.
Nick Winter
> I would consider averaging the variables, after reversing the coding
for
> the ones that are coded with opposite "sense". (e.g., so that
higher
> scores on each indicates more tolerant attitudes)
> Look at egen rmean(...)
Why do you need a composite measure? It is often a good way
of blurring important distinctions. If in fact these measures
are highly related, then one will serve as well as any other.
If, as seems a little more likely, they measure rather
different things, it is not clear that any composite measure
will add much to looking separately at your different responses.
In any case, any kind of averaging (means or PCA) has to be smart
about
don't knows and missings, which I guess are coded higher
than the other values. At first sight, the only clean way
to deal with those is to omit any observation with any don't know
or missing from the averaging.
Also contemplate
gra deasyblk dwelfblk dintlblk drichblk, matrix j(1)
Nick
[email protected]
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